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The development of statistical/machine learning approaches for downscaling at the kilometer scale will be the main mission of the position. For various climate variables (temperature, precipitation, wind, etc
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molecular simulations, machine-learning techniques, and statistical mechanics for research opportunities in: Development of data-driven schemes for the discovery of slow degrees of freedom Molecular
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Postdoctoral position (M/F): Machine learning design of alloys for concentrated solar energy storage
mission will be to develop machine learning models to predict properties of alloys of elements of groups 1 to 15, such as their melting temperature, range, and enthalpy. Based on these predictive models
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approach and framed as a continuous improvement process, and (3) on machine learning algorithms guided by theory and analogues from natural objects and simulations. The proposed position will cover four
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of learning dynamic systems and physically informed neural networks (PINNs), for application to neuroscience research. The main task of the postdoctoral fellow will be to develop models for modeling
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contribute to the ERC Advanced Project COGNIBRAINS, which aims at elucidating the neural mechanisms underlying simple and high-level forms of visual learning in the brain of bees, placed in a virtual reality
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machine learning have transformed our approach to inverse problems in various fields, notably in medical imaging, enabling a deeper understanding of complex data structures. However, although sophisticated
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and apply Deep Learning tools for protein modeling, small molecule docking, and structure prediction. • Collaborate with interdisciplinary teams to advance research goals and contribute to scientific
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of the following topics will be appreciated: · SAT solving, · Problem encodings and reformulation, · Cryptography, · Pattern mining and machine learning. Website for additional job details
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and apply Deep Learning tools for protein modeling, small molecule docking, and structure prediction. • Collaborate with interdisciplinary teams to advance research goals and contribute to scientific